Data profiler

ABSTRACT

One or more techniques and/or systems are provided for profiling a dataset. For example, a snapshot of a volume may be evaluated to identify a set of data characteristics, such as file and directory size information. A baseline dataset profile of a dataset of data within the volume may be constructed based upon the set of data characteristics. Histograms and graphs of directory counts and file counts may be constructed based upon the baseline dataset profile. An incremental dataset profile may be constructed for the dataset based upon an evaluation of the snapshot and a subsequent snapshot of the volume. Histograms and graphs of directories and files that are modified, created, and/or deleted may be constructed based upon the incremental dataset profile. Performance predictions, analytics, field diagnostics of performance issues, and/or scheduling of service execution may be implemented for a storage network hosting the volume based upon dataset profiles.

RELATED APPLICATION

This application claims priority to and is a continuation of U.S. patentapplication Ser. No. 14/836,259, filed on Aug. 26, 2015 and titled “DATAPROFILER,” which is incorporated herein by reference.

BACKGROUND

A storage network environment may provide clients with access to userdata stored across one or more storage devices. For example, a clusternetwork environment may comprise one or more storage clusters of storagecontrollers (e.g., nodes) configured to provide clients with access touser data stored within storage devices. Various data managementservices may be implemented for the storage network environment, such asbackup and restore functionality, replication functionality, snapshotfunctionality, dump commands, etc. Performance of such services maydepend on factors such as characteristics of a dataset upon which a datamanagement service is operating. For example, file sizes, directorysizes, numbers of files, numbers of directories, data changes (e.g.,files and directories of a volume that are added, removed, or modifiedsince a prior snapshot of a volume), and/or a variety of othercharacteristics of the dataset may affect performance of a replicationdata management service.

Data management services may utilize computing resources and bandwidthof the storage network environment for operation. For example, thereplication data management service may be hosted on a storagecontroller, and may be configured to send data over a network to asecond storage controller for data replication. Inefficient operation ofdata management services due to characteristics of datasets may degradeperformance of users accessing data through the storage networkenvironment. For example, the storage controller may have inadequatecomputing resources and/or bandwidth available for processing client I/Orequests to data because the storage controller is executing thereplication data management service. Unfortunately, a client, for whichthe storage network environment is maintained, may be unable to providean administrator of the storage network environment with a clear view ofa profile of the dataset, and thus the administrator may be unable todiagnose the degraded performance.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a component block diagram illustrating an example clusterednetwork in accordance with one or more of the provisions set forthherein.

FIG. 2 is a component block diagram illustrating an example data storagesystem in accordance with one or more of the provisions set forthherein.

FIG. 3 is a flow chart illustrating an exemplary method of profiling adataset.

FIG. 4 is a component block diagram illustrating an exemplary system forprofiling a dataset to create a baseline dataset profile.

FIG. 5 is a component block diagram illustrating an exemplary system forprofiling a dataset to create a graph of directory counts.

FIG. 6 is a component block diagram illustrating an exemplary system forprofiling a dataset to create a graph of file counts.

FIG. 7 is a component block diagram illustrating an exemplary system forprofiling a dataset to create an incremental dataset profile.

FIG. 8 is a component block diagram illustrating an exemplary system forprofiling a dataset to create a set of directory graphs.

FIG. 9 is a component block diagram illustrating an exemplary system forprofiling a dataset to create a set of file graphs.

FIG. 10 is an example of a computer readable medium in accordance withone or more of the provisions set forth herein.

DETAILED DESCRIPTION

Some examples of the claimed subject matter are now described withreference to the drawings, where like reference numerals are generallyused to refer to like elements throughout. In the following description,for purposes of explanation, numerous specific details are set forth inorder to provide an understanding of the claimed subject matter. It maybe evident, however, that the claimed subject matter may be practicedwithout these specific details. Nothing in this detailed description isadmitted as prior art.

One or more systems and/or techniques for profiling a dataset areprovided. A dataset profiler may be configured to evaluate snapshots ofa volume for creating a baseline dataset profile and/or incrementaldataset profiles (e.g., changes to the volume since the baseline datasetprofile was created). In an example, the dataset profiler may evaluate asnapshot of the volume to identify a set of data characteristics, suchas file size information and directory size information, forconstructing a baseline dataset profile of a dataset of data within thevolume. In another example, the dataset profiler may evaluate thesnapshot and a second snapshot of the volume (e.g., a subsequentsnapshot at a later point in time from when the snapshot was captured)to identify a second set of data characteristics, corresponding todifferences between the snapshot and the second snapshot, forconstructing an incremental dataset profile of the dataset.

Dataset profiles may be used for various purposes within a data storageenvironment. In an example, histograms of file sizes and directorysizes, a graph of directory counts, and/or a graph of file counts may beconstructed from the baseline dataset profile. Histograms of file sizesand/or directory sizes that are modified, created, and/or deleted may beconstructed based upon the incremental dataset profile. In anotherexample, a dataset profile may be used to better understand clientworkloads and what types of data is produced by such workloads (e.g.,files sizes, directory structures, directory depth, number of directoryentries, etc.). In another example, diagnosis of performance issueswithin the data storage environment may be efficiently performed using adataset profile because the dataset profile may specify file sizes,directory sizes, and/or other data characteristics that may beindicative of whether performance of a service may be affected by suchdata characteristics (e.g., a replication service may perform slowerwhen replicating a large number of small modified files as opposed to asmall number of large files), and thus services such as the replicationservice may be efficiently scheduled (e.g., slower and/or resourceintensive services may be scheduled for execution by a storagecontroller during periods of low client load on the storage controller).In another example, a dataset profile may be evaluated to detectviolations of recommendations (e.g., file size recommendations, filelocation recommendations, etc.) and/or service level objectives (e.g., aclient I/O latency violation due to a service operating on a type ofdata that hinders performance of the service). In another example,modification percentage and modified file block number (FBN) ranges infiles may be reported based upon a data profile. In this way, advancedanalytics may be performed on the dataset, such as to determine howservices may affect client access performance to data managed by thedata storage network (e.g., a determination that a replication servicemay expend additional resources of a storage controller to perform areplication due to particular characteristics of files and/ordirectories, and thus the storage controller may provide slower clientI/O processing).

To provide context for profiling a dataset, FIG. 1 illustrates anembodiment of a clustered network environment 100 or a network storageenvironment. It may be appreciated, however, that the techniques, etc.described herein may be implemented within the clustered networkenvironment 100, a non-cluster network environment, and/or a variety ofother computing environments, such as a desktop computing environment.That is, the instant disclosure, including the scope of the appendedclaims, is not meant to be limited to the examples provided herein. Itwill be appreciated that where the same or similar components, elements,features, items, modules, etc. are illustrated in later figures but werepreviously discussed with regard to prior figures, that a similar (e.g.,redundant) discussion of the same may be omitted when describing thesubsequent figures (e.g., for purposes of simplicity and ease ofunderstanding).

FIG. 1 is a block diagram illustrating an example clustered networkenvironment 100 that may implement at least some embodiments of thetechniques and/or systems described herein. The example environment 100comprises data storage systems or storage sites 102 and 104 that arecoupled over a cluster fabric 106, such as a computing network embodiedas a private Infiniband, Fibre Channel (FC), or Ethernet networkfacilitating communication between the storage systems 102 and 104 (andone or more modules, component, etc. therein, such as, nodes 116 and118, for example). It will be appreciated that while two data storagesystems 102 and 104 and two nodes 116 and 118 are illustrated in FIG. 1,that any suitable number of such components is contemplated. In anexample, nodes 116, 118 comprise storage controllers (e.g., node 116 maycomprise a primary or local storage controller and node 118 may comprisea secondary or remote storage controller) that provide client devices,such as host devices 108, 110, with access to data stored within datastorage devices 128, 130. Similarly, unless specifically providedotherwise herein, the same is true for other modules, elements,features, items, etc. referenced herein and/or illustrated in theaccompanying drawings. That is, a particular number of components,modules, elements, features, items, etc. disclosed herein is not meantto be interpreted in a limiting manner.

It will be further appreciated that clustered networks are not limitedto any particular geographic areas and can be clustered locally and/orremotely. Thus, in one embodiment a clustered network can be distributedover a plurality of storage systems and/or nodes located in a pluralityof geographic locations; while in another embodiment a clustered networkcan include data storage systems (e.g., 102, 104) residing in a samegeographic location (e.g., in a single onsite rack of data storagedevices).

In the illustrated example, one or more host devices 108, 110 which maycomprise, for example, client devices, personal computers (PCs),computing devices used for storage (e.g., storage servers), and othercomputers or peripheral devices (e.g., printers), are coupled to therespective data storage systems 102, 104 by storage network connections112, 114. Network connection may comprise a local area network (LAN) orwide area network (WAN), for example, that utilizes Network AttachedStorage (NAS) protocols, such as a Common Internet File System (CIFS)protocol or a Network File System (NFS) protocol to exchange datapackets. Illustratively, the host devices 108, 110 may begeneral-purpose computers running applications, and may interact withthe data storage systems 102, 104 using a client/server model forexchange of information. That is, the host device may request data fromthe data storage system (e.g., data on a storage device managed by anetwork storage control configured to process I/O commands issued by thehost device for the storage device), and the data storage system mayreturn results of the request to the host device via one or more networkconnections 112, 114.

The nodes 116, 118 on clustered data storage systems 102, 104 cancomprise network or host nodes that are interconnected as a cluster toprovide data storage and management services, such as to an enterprisehaving remote locations, cloud storage (e.g., a storage endpoint may bestored within a data cloud), etc., for example. Such a node in a datastorage and management network cluster environment 100 can be a deviceattached to the network as a connection point, redistribution point orcommunication endpoint, for example. A node may be capable of sending,receiving, and/or forwarding information over a network communicationschannel, and could comprise any device that meets any or all of thesecriteria. One example of a node may be a data storage and managementserver attached to a network, where the server can comprise a generalpurpose computer or a computing device particularly configured tooperate as a server in a data storage and management system.

In an example, a first cluster of nodes such as the nodes 116, 118(e.g., a first set of storage controllers configured to provide accessto a first storage aggregate comprising a first logical grouping of oneor more storage devices) may be located on a first storage site. Asecond cluster of nodes, not illustrated, may be located at a secondstorage site (e.g., a second set of storage controllers configured toprovide access to a second storage aggregate comprising a second logicalgrouping of one or more storage devices). The first cluster of nodes andthe second cluster of nodes may be configured according to a disasterrecovery configuration where a surviving cluster of nodes providesswitchover access to storage devices of a disaster cluster of nodes inthe event a disaster occurs at a disaster storage site comprising thedisaster cluster of nodes (e.g., the first cluster of nodes providesclient devices with switchover data access to storage devices of thesecond storage aggregate in the event a disaster occurs at the secondstorage site).

As illustrated in the exemplary environment 100, nodes 116, 118 cancomprise various functional components that coordinate to providedistributed storage architecture for the cluster. For example, the nodescan comprise a network module 120, 122 and a data module 124, 126.Network modules 120, 122 can be configured to allow the nodes 116, 118(e.g., network storage controllers) to connect with host devices 108,110 over the network connections 112, 114, for example, allowing thehost devices 108, 110 to access data stored in the distributed storagesystem. Further, the network modules 120, 122 can provide connectionswith one or more other components through the cluster fabric 106. Forexample, in FIG. 1, a first network module 120 of first node 116 canaccess a second data storage device 130 by sending a request through asecond data module 126 of a second node 118.

Data modules 124, 126 can be configured to connect one or more datastorage devices 128, 130, such as disks or arrays of disks, flashmemory, or some other form of data storage, to the nodes 116, 118. Thenodes 116, 118 can be interconnected by the cluster fabric 106, forexample, allowing respective nodes in the cluster to access data on datastorage devices 128, 130 connected to different nodes in the cluster.Often, data modules 124, 126 communicate with the data storage devices128, 130 according to a storage area network (SAN) protocol, such asSmall Computer System Interface (SCSI) or Fiber Channel Protocol (FCP),for example. Thus, as seen from an operating system on a node 116, 118,the data storage devices 128, 130 can appear as locally attached to theoperating system. In this manner, different nodes 116, 118, etc. mayaccess data blocks through the operating system, rather than expresslyrequesting abstract files.

It should be appreciated that, while the example embodiment 100illustrates an equal number of network and data modules, otherembodiments may comprise a differing number of these modules. Forexample, there may be a plurality of network and data modulesinterconnected in a cluster that does not have a one-to-onecorrespondence between the network and data modules. That is, differentnodes can have a different number of network and data modules, and thesame node can have a different number of network modules than datamodules.

Further, a host device 108, 110 can be networked with the nodes 116, 118in the cluster, over the networking connections 112, 114. As an example,respective host devices 108, 110 that are networked to a cluster mayrequest services (e.g., exchanging of information in the form of datapackets) of a node 116, 118 in the cluster, and the node 116, 118 canreturn results of the requested services to the host devices 108, 110.In one embodiment, the host devices 108, 110 can exchange informationwith the network modules 120, 122 residing in the nodes (e.g., networkhosts) 116, 118 in the data storage systems 102, 104.

In one embodiment, the data storage devices 128, 130 comprise volumes132, which is an implementation of storage of information onto diskdrives or disk arrays or other storage (e.g., flash) as a file-systemfor data, for example. Volumes can span a portion of a disk, acollection of disks, or portions of disks, for example, and typicallydefine an overall logical arrangement of file storage on disk space inthe storage system. In one embodiment a volume can comprise stored dataas one or more files that reside in a hierarchical directory structurewithin the volume.

Volumes are typically configured in formats that may be associated withparticular storage systems, and respective volume formats typicallycomprise features that provide functionality to the volumes, such asproviding an ability for volumes to form clusters. For example, where afirst storage system may utilize a first format for their volumes, asecond storage system may utilize a second format for their volumes.

In the example environment 100, the host devices 108, 110 can utilizethe data storage systems 102, 104 to store and retrieve data from thevolumes 132. In this embodiment, for example, the host device 108 cansend data packets to the network module 120 in the node 116 within datastorage system 102. The node 116 can forward the data to the datastorage device 128 using the data module 124, where the data storagedevice 128 comprises volume 132A. In this way, in this example, the hostdevice can access the storage volume 132A, to store and/or retrievedata, using the data storage system 102 connected by the networkconnection 112. Further, in this embodiment, the host device 110 canexchange data with the network module 122 in the host 118 within thedata storage system 104 (e.g., which may be remote from the data storagesystem 102). The host 118 can forward the data to the data storagedevice 130 using the data module 126, thereby accessing volume 1328associated with the data storage device 130.

It may be appreciated that profiling a dataset may be implemented withinthe clustered network environment 100. For example, a data profilercomponent may be implemented for the node 116 and/or the node 118. Thedata profiler component may be configured to evaluate snapshots of thevolume 132A and/or the volume 1328 to construct dataset profiles of datawithin such volumes. In may be appreciated that profiling a dataset maybe implemented for and/or between any type of computing environment, andmay be transferrable between physical devices (e.g., node 116, node 118,etc.) and/or a cloud computing environment (e.g., remote to theclustered network environment 100).

FIG. 2 is an illustrative example of a data storage system 200 (e.g.,102, 104 in FIG. 1), providing further detail of an embodiment ofcomponents that may implement one or more of the techniques and/orsystems described herein. The example data storage system 200 comprisesa node 202 (e.g., host nodes 116, 118 in FIG. 1), and a data storagedevice 234 (e.g., data storage devices 128, 130 in FIG. 1). The node 202may be a general purpose computer, for example, or some other computingdevice particularly configured to operate as a storage server. A hostdevice 205 (e.g., 108, 110 in FIG. 1) can be connected to the node 202over a network 216, for example, to provides access to files and/orother data stored on the data storage device 234. In an example, thenode 202 comprises a storage controller that provides client devices,such as the host device 205, with access to data stored within datastorage device 234.

The data storage device 234 can comprise mass storage devices, such asdisks 224, 226, 228 of a disk array 218, 220, 222. It will beappreciated that the techniques and systems, described herein, are notlimited by the example embodiment. For example, disks 224, 226, 228 maycomprise any type of mass storage devices, including but not limited tomagnetic disk drives, flash memory, and any other similar media adaptedto store information, including, for example, data (D) and/or parity (P)information.

The node 202 comprises one or more processors 204, a memory 206, anetwork adapter 210, a cluster access adapter 212, and a storage adapter214 interconnected by a system bus 242. The storage system 200 alsoincludes an operating system 208 installed in the memory 206 of the node202 that can, for example, implement a Redundant Array of Independent(or Inexpensive) Disks (RAID) optimization technique to optimize areconstruction process of data of a failed disk in an array.

The operating system 208 can also manage communications for the datastorage system, and communications between other data storage systemsthat may be in a clustered network, such as attached to a cluster fabric215 (e.g., 106 in FIG. 1). Thus, the node 202, such as a network storagecontroller, can respond to host device requests to manage data on thedata storage device 234 (e.g., or additional clustered devices) inaccordance with these host device requests. The operating system 208 canoften establish one or more file systems on the data storage system 200,where a file system can include software code and data structures thatimplement a persistent hierarchical namespace of files and directories,for example. As an example, when a new data storage device (not shown)is added to a clustered network system, the operating system 208 isinformed where, in an existing directory tree, new files associated withthe new data storage device are to be stored. This is often referred toas “mounting” a file system.

In the example data storage system 200, memory 206 can include storagelocations that are addressable by the processors 204 and adapters 210,212, 214 for storing related software application code and datastructures. The processors 204 and adapters 210, 212, 214 may, forexample, include processing elements and/or logic circuitry configuredto execute the software code and manipulate the data structures. Theoperating system 208, portions of which are typically resident in thememory 206 and executed by the processing elements, functionallyorganizes the storage system by, among other things, invoking storageoperations in support of a file service implemented by the storagesystem. It will be apparent to those skilled in the art that otherprocessing and memory mechanisms, including various computer readablemedia, may be used for storing and/or executing application instructionspertaining to the techniques described herein. For example, theoperating system can also utilize one or more control files (not shown)to aid in the provisioning of virtual machines.

The network adapter 210 includes the mechanical, electrical andsignaling circuitry needed to connect the data storage system 200 to ahost device 205 over a computer network 216, which may comprise, amongother things, a point-to-point connection or a shared medium, such as alocal area network. The host device 205 (e.g., 108, 110 of FIG. 1) maybe a general-purpose computer configured to execute applications. Asdescribed above, the host device 205 may interact with the data storagesystem 200 in accordance with a client/host model of informationdelivery.

The storage adapter 214 cooperates with the operating system 208executing on the node 202 to access information requested by the hostdevice 205 (e.g., access data on a storage device managed by a networkstorage controller). The information may be stored on any type ofattached array of writeable media such as magnetic disk drives, flashmemory, and/or any other similar media adapted to store information. Inthe example data storage system 200, the information can be stored indata blocks on the disks 224, 226, 228. The storage adapter 214 caninclude input/output (I/O) interface circuitry that couples to the disksover an I/O interconnect arrangement, such as a storage area network(SAN) protocol (e.g., Small Computer System Interface (SCSI), iSCSI,hyperSCSI, Fiber Channel Protocol (FCP)). The information is retrievedby the storage adapter 214 and, if necessary, processed by the one ormore processors 204 (or the storage adapter 214 itself) prior to beingforwarded over the system bus 242 to the network adapter 210 (and/or thecluster access adapter 212 if sending to another node in the cluster)where the information is formatted into a data packet and returned tothe host device 205 over the network connection 216 (and/or returned toanother node attached to the cluster over the cluster fabric 215).

In one embodiment, storage of information on arrays 218, 220, 222 can beimplemented as one or more storage “volumes” 230, 232 that are comprisedof a cluster of disks 224, 226, 228 defining an overall logicalarrangement of disk space. The disks 224, 226, 228 that comprise one ormore volumes are typically organized as one or more groups of RAIDs. Asan example, volume 230 comprises an aggregate of disk arrays 218 and220, which comprise the cluster of disks 224 and 226.

In one embodiment, to facilitate access to disks 224, 226, 228, theoperating system 208 may implement a file system (e.g., write anywherefile system) that logically organizes the information as a hierarchicalstructure of directories and files on the disks. In this embodiment,respective files may be implemented as a set of disk blocks configuredto store information, whereas directories may be implemented asspecially formatted files in which information about other files anddirectories are stored.

Whatever the underlying physical configuration within this data storagesystem 200, data can be stored as files within physical and/or virtualvolumes, which can be associated with respective volume identifiers,such as file system identifiers (FSIDs), which can be 32-bits in lengthin one example.

A physical volume corresponds to at least a portion of physical storagedevices whose address, addressable space, location, etc. doesn't change,such as at least some of one or more data storage devices 234 (e.g., aRedundant Array of Independent (or Inexpensive) Disks (RAID system)).Typically the location of the physical volume doesn't change in that the(range of) address(es) used to access it generally remains constant.

A virtual volume, in contrast, is stored over an aggregate of disparateportions of different physical storage devices. The virtual volume maybe a collection of different available portions of different physicalstorage device locations, such as some available space from each of thedisks 224, 226, and/or 228. It will be appreciated that since a virtualvolume is not “tied” to any one particular storage device, a virtualvolume can be said to include a layer of abstraction or virtualization,which allows it to be resized and/or flexible in some regards.

Further, a virtual volume can include one or more logical unit numbers(LUNs) 238, directories 236, Qtrees 235, and files 240. Among otherthings, these features, but more particularly LUNS, allow the disparatememory locations within which data is stored to be identified, forexample, and grouped as data storage unit. As such, the LUNs 238 may becharacterized as constituting a virtual disk or drive upon which datawithin the virtual volume is stored within the aggregate. For example,LUNs are often referred to as virtual drives, such that they emulate ahard drive from a general purpose computer, while they actually comprisedata blocks stored in various parts of a volume.

In one embodiment, one or more data storage devices 234 can have one ormore physical ports, wherein each physical port can be assigned a targetaddress (e.g., SCSI target address). To represent respective volumesstored on a data storage device, a target address on the data storagedevice can be used to identify one or more LUNs 238. Thus, for example,when the node 202 connects to a volume 230, 232 through the storageadapter 214, a connection between the node 202 and the one or more LUNs238 underlying the volume is created.

In one embodiment, respective target addresses can identify multipleLUNs, such that a target address can represent multiple volumes. The I/Ointerface, which can be implemented as circuitry and/or software in thestorage adapter 214 or as executable code residing in memory 206 andexecuted by the processors 204, for example, can connect to volume 230by using one or more addresses that identify the LUNs 238.

It may be appreciated that profiling a dataset may be implemented forthe data storage system 200. For example, a data profiler component maybe implemented for the node 202. The data profiler component may beconfigured to evaluate snapshots of the volume 230 and/or the volume 232to construct dataset profiles of data within such volumes. In may beappreciated that profiling a dataset may be implemented for and/orbetween any type of computing environment, and may be transferrablebetween physicals devices (e.g., node 202, another node associated withthe data storage system 200, etc.) and/or a cloud computing environment(e.g., remote to the data storage system 200).

One embodiment of profiling a dataset is illustrated by an exemplarymethod 300 of FIG. 3. At 302, a snapshot of a volume (e.g., a point intime representation of the volume) may be evaluated (e.g., utilizing asnapshot difference tool, such as a snapshot difference API that isinvoked using a volume identifier of the volume and the snapshot asarguments) to identify a set of data characteristics. The set of datacharacteristics may comprise file size information, directory sizeinformation, file count information, directory count information, anumber of inodes, a size of inodes, and/or other data characteristics.

At 304, a baseline dataset profile of a dataset of data within thevolume may be constructed based upon the set of data characteristics.The baseline dataset profile may specify file counts, files sizes,directory counts, directory sizes, and/or other information. In anexample, a histogram of file sizes and directory sizes of the datasetmay be constructed based upon the baseline dataset profile. In anotherexample, a graph of directory counts may be constructed (e.g., counts ofdirectories having certain sizes or other characteristics such asnumbers of files or subdirectories comprised therein). In anotherexample, a graph of file counts may be constructed (e.g., counts offiles having certain sizes).

In an example of creating dataset profiles, the snapshot and a secondsnapshot of the volume (e.g., the second snapshot corresponding a secondpoint in time representation of the volume that is subsequent to thepoint in time representation of the volume within the snapshot) may beevaluated (e.g., utilizing the snapshot difference tool, such as thesnapshot difference API to loop through the snapshots for identifyingdifferences) to identify a second set of data characteristicscorresponding to differences between the snapshot and the secondsnapshot. The second set of data characteristics may comprise directorycreation information, directory deletion information, directorymodification information, file creation information, file deletioninformation, file modification information, and/or other informationregarding differences in the dataset of the volume from the point intime of the snapshot and the second point in time of the secondsnapshot. In this way, an incremental dataset profile of the dataset maybe constructed based upon the second set of data characteristics.Histograms and/or graphs of files of certain sizes that are modified,created, and/or deleted may be constructed based upon the incrementaldataset profile. Histograms and/or graphs of directory of certain sizesthat are modified, created, and/or deleted may be constructed based uponthe incremental dataset profile.

In an example, a data management performance issue of a data storageenvironment may be identified based upon the baseline dataset profileand/or the incremental dataset profile. For example, a dataset profilemay indicate that the dataset comprises a large number of small modifiedfiles dispersed amongst a large number of nested directories. Acorrective action may be implemented for the data management performanceissue (e.g., a user may be notified of the data management performanceissue; a volume resize suggestion may be provided so that a volume doesnot exceed a recommended volume size; a replication service may bescheduled for time period where additional resources are available;etc.). A replication service, hosted on a storage controller of the datastorage environment, may execute longer and/or consume more resources ofthe storage controller when attempting to replicate such a dataset ascompared to datasets having other characteristics, and thus the storagecontroller may provide degraded performance for servicing client I/Orequests. In this way, a backup command, a replication command, and/orother services may be scheduled based upon the dataset profile, such aswhere the replication service is scheduled to execute overnight when thestorage controller handles a low volume of client I/O requests.

In an example, the baseline dataset profile and/or the incrementaldataset profile may be evaluated to identify a service level objectiveviolation of a data management function operating on the dataset. Forexample, a service level objective may specify a threshold amount oftime before a disaster during which data is allowed to be lost. A writecaching replication service for replicating cached data of the volumebetween storage controllers may be unable to achieve the service levelobject because the dataset, which is indicative of types of data cachedand flushed to the volume during consistency points, may have a dataprofile that increases job execution time of the write cachingreplication service. Thus, a notification of the service level objectiveviolation may be provided to a storage administrator or client.

In another example, the baseline dataset profile and/or the incrementaldataset profile may be evaluated to prevent a Quality of Service (QoS)false alarm (e.g., a dataset profiler component may be executed througha QoS background class such that the dataset profiler component mayyield of other threads and workloads in the event of a contention forresources). In another example, the baseline dataset profile and/or theincremental dataset profile may be evaluated to identify a datamanagement recommendation violation. The data management recommendationviolation may correspond to a file location violation, a logical unitnumber (LUN) location violation, a volume size violation, a directorysize violation, a file size violation, a file count violation, adirectory count violation, and/or other violations of recommendationsfor managing storage of the network storage environment.

In an example, construction of baseline dataset profiles and/orincremental dataset profiles may be performed on-demand in response toon-demand data profile commands. In another example, a profile creationschedule, for creating dataset profiles over time, may be implemented.In this way, dataset profiles may be created and/or evaluated forperformance prediction, efficient field diagnosis of performance issues,analytical evaluation of how data is structures and stored, scheduledmaintenance of backups, detection of recommendation violations,mitigation of false alarms from QoS, etc.

FIG. 4 illustrates an example of a system 400 for profiling a dataset.The system 400 comprises a dataset profiler component 404. The datasetprofiler component 404 may be configured to evaluate a snapshot 402 of avolume, such as a point in time representation of the volume. Thedataset profiler component 404 may evaluate the snapshot 402 (e.g.,using a snapshot difference API) to identify a set of datacharacteristics 406 comprising file size information, directory sizeinformation, and/or other information about the volume and data storedtherein. The dataset profiler component 404 may construct a baselinedataset profile 408 based upon the set of data characteristics 406. Thebaseline dataset profile 408 may specify file size information, filecount information, directory size information, directory countinformation, inode information, etc.

FIG. 5 illustrates an example of a system 500 for profiling a dataset.The system 500 comprises a dataset profiler component 502. The datasetprofiler component 502 may have constructed a baseline dataset profile504 of a volume. The dataset profiler component 502 may construct agraph 506 of directory counts of the volume based upon the baselinedataset profile 504. The graph 506 may comprise an x-axis representingdirectory sizes and a y-axis representing counts of directories havingparticular directory sizes (e.g., 5 directories of the volume may havesizes equal to 4K or less; 1 directory of the volume may have a size of128M or greater; etc.).

FIG. 6 illustrates an example of a system 600 for profiling a dataset.The system 600 comprises a dataset profiler component 602. The datasetprofiler component 602 may have constructed a baseline dataset profile604 of a volume. The dataset profiler component 602 may construct agraph 606 of file counts of the volume based upon the baseline datasetprofile 604. The graph 606 may comprise an x-axis representing filesizes and a y-axis representing counts of files having particular filesizes (e.g., 200,000 files may have file sizes between 0 and 64 bytes).

FIG. 7 illustrates an example of a system 700 for profiling a dataset.The system 700 comprises a dataset profiler component 706. The datasetprofiler component 706 may be configured to evaluate a snapshot 702 of avolume, such as a point in time representation of the volume, and asecond snapshot 704 of the volume, such as a subsequent point in timerepresentation of the volume. The dataset profiler component 706 mayevaluate the snapshot 702 and the second snapshot 704 to identify a setof data characteristics 708 corresponding to differences between thesnapshot 702 and the second snapshot 704 and/or other information aboutthe volume and data stored therein. The dataset profiler component 706may construct an incremental dataset profile 710 based upon the set ofdata characteristics 709. The incremental dataset profile 710 mayspecify directory creation information, directory deletion information,directory modification information, file creation information, filedeletion information, file modification information, and/or otherinformation relating to size and count differences of data of the volumebetween the point in time at which the snapshot 702 was captured and thesubsequent point in time at which the second snapshot 704 was captured.

FIG. 8 illustrates an example of a system 800 for profiling a dataset.The system 800 comprises a dataset profiler component 802. The datasetprofiler component 802 may have constructed an incremental datasetprofile 804 of a volume. The dataset profiler component 802 mayconstruct graphs 806, such as a directory creation graph 808, adirectory deletion graph 810, and a directory modified graph 812, basedupon the incremental dataset profile 804. The directory creation graph808 may comprise an x-axis representing directory sizes and a y-axisrepresenting counts of directories, having particular directory sizes,that were created (e.g., 10 new directories, having sizes equal to 4K orless, were created between a point in time of a snapshot of the volumeand a subsequent point in time of a second snapshot of the volume). Thedirectory deletion graph 810 may comprise an x-axis representingdirectory sizes and a y-axis representing counts of directories, havingparticular directory sizes, that were deleted (e.g., 7 directories,having sizes equal to 4K or less, were deleted between the point in timeand the subsequent point in time of the volume). The directorymodification graph 812 may comprise an x-axis representing directorysizes and a y-axis representing counts of directories, having particulardirectory sizes, that were modified (e.g., 6 directories, having sizesequal to 4K or less, were modified between the point in time and thesubsequent point in time of the volume).

FIG. 9 illustrates an example of a system 900 for profiling a dataset.The system 900 comprises a dataset profiler component 902. The datasetprofiler component 902 may have constructed an incremental datasetprofile 904 of a volume. The dataset profiler component 902 mayconstruct graphs 906, such as a file creation graph 908, a file deletiongraph 910, and a file modified graph 912, based upon the incrementaldataset profile 904. The file creation graph 908 may comprise an x-axisrepresenting file sizes and a y-axis representing counts of files,having particular file sizes, that were created (e.g., 9,000 new files,having sizes equal to 0 bytes, were created between a point in time of asnapshot of the volume and a subsequent point in time of a secondsnapshot of the volume). The file deletion graph 910 may comprise anx-axis representing file sizes and a y-axis representing counts offiles, having particular file sizes, that were deleted (e.g., 5,000files, having sizes equal to 0 bytes, were deleted between the point intime and the subsequent point in time of the volume). The filemodification graph 912 may comprise an x-axis representing file sizesand a y-axis representing counts of files, having particular file sizes,that were modified (e.g., 7,000 files, having sizes equal to 0 bytes,were modified between the point in time and the subsequent point in timeof the volume).

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to implement one or more ofthe techniques presented herein. An example embodiment of acomputer-readable medium or a computer-readable device that is devisedin these ways is illustrated in FIG. 10, wherein the implementation 1000comprises a computer-readable medium 1008, such as a CD-R, DVD-R, flashdrive, a platter of a hard disk drive, etc., on which is encodedcomputer-readable data 1006. This computer-readable data 1006, such asbinary data comprising at least one of a zero or a one, in turncomprises a set of computer instructions 1004 configured to operateaccording to one or more of the principles set forth herein. In someembodiments, the processor-executable computer instructions 1004 areconfigured to perform a method 1002, such as at least some of theexemplary method 300 of FIG. 3, for example. In some embodiments, theprocessor-executable instructions 1004 are configured to implement asystem, such as at least some of the exemplary system 400 of FIG. 4, atleast some of the exemplary system 500 of FIG. 5, at least some of theexemplary system 600 of FIG. 6, at least some of the exemplary system700 of FIG. 7, at least some of the exemplary system 800 of FIG. 8,and/or at least some of the exemplary system 900 of FIG. 9, for example.Many such computer-readable media are contemplated to operate inaccordance with the techniques presented herein.

It will be appreciated that processes, architectures and/or proceduresdescribed herein can be implemented in hardware, firmware and/orsoftware. It will also be appreciated that the provisions set forthherein may apply to any type of special-purpose computer (e.g., filehost, storage server and/or storage serving appliance) and/orgeneral-purpose computer, including a standalone computer or portionthereof, embodied as or including a storage system. Moreover, theteachings herein can be configured to a variety of storage systemarchitectures including, but not limited to, a network-attached storageenvironment and/or a storage area network and disk assembly directlyattached to a client or host computer. Storage system should thereforebe taken broadly to include such arrangements in addition to anysubsystems configured to perform a storage function and associated withother equipment or systems.

In some embodiments, methods described and/or illustrated in thisdisclosure may be realized in whole or in part on computer-readablemedia. Computer readable media can include processor-executableinstructions configured to implement one or more of the methodspresented herein, and may include any mechanism for storing this datathat can be thereafter read by a computer system. Examples of computerreadable media include (hard) drives (e.g., accessible via networkattached storage (NAS)), Storage Area Networks (SAN), volatile andnon-volatile memory, such as read-only memory (ROM), random-accessmemory (RAM), EEPROM and/or flash memory, CD-ROMs, CD-Rs, CD-RWs, DVDs,cassettes, magnetic tape, magnetic disk storage, optical or non-opticaldata storage devices and/or any other medium which can be used to storedata.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of implementing at least some of the claims.

Various operations of embodiments are provided herein. The order inwhich some or all of the operations are described should not beconstrued to imply that these operations are necessarily orderdependent. Alternative ordering will be appreciated given the benefit ofthis description. Further, it will be understood that not all operationsare necessarily present in each embodiment provided herein. Also, itwill be understood that not all operations are necessary in someembodiments.

Furthermore, the claimed subject matter is implemented as a method,apparatus, or article of manufacture using standard application orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer application accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

As used in this application, the terms “component”, “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentincludes a process running on a processor, a processor, an object, anexecutable, a thread of execution, an application, or a computer. By wayof illustration, both an application running on a controller and thecontroller can be a component. One or more components residing within aprocess or thread of execution and a component may be localized on onecomputer or distributed between two or more computers.

Moreover, “exemplary” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused in this application, “or” is intended to mean an inclusive “or”rather than an exclusive “or”. In addition, “a” and “an” as used in thisapplication are generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Also, at least one of A and B and/or the like generally means A orB and/or both A and B. Furthermore, to the extent that “includes”,“having”, “has”, “with”, or variants thereof are used, such terms areintended to be inclusive in a manner similar to the term “comprising”.

Many modifications may be made to the instant disclosure withoutdeparting from the scope or spirit of the claimed subject matter. Unlessspecified otherwise, “first,” “second,” or the like are not intended toimply a temporal aspect, a spatial aspect, an ordering, etc. Rather,such terms are merely used as identifiers, names, etc. for features,elements, items, etc. For example, a first set of information and asecond set of information generally correspond to set of information Aand set of information B or two different or two identical sets ofinformation or the same set of information.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A method, comprising: evaluating, by a storageserver, a snapshot of a volume to identify a set of data characteristicscomprising file size information and directory size information;constructing a baseline dataset profile of a dataset of data within thevolume based upon the set of data characteristics; and constructing afirst histogram of file sizes and directory sizes of the dataset basedupon the baseline dataset profile.
 2. The method of claim 1, comprising:evaluating the snapshot and a second snapshot of the volume to identifya second set of data characteristics corresponding to differencesbetween the snapshot and the second snapshot; constructing anincremental dataset profile of the dataset based upon the second set ofdata characteristics; and constructing a second histogram of file sizesthat are modified, created, and deleted based upon the incrementaldataset profile.
 3. The method of claim 1, comprising: evaluating thesnapshot and a second snapshot of the volume to identify a second set ofdata characteristics corresponding to differences between the snapshotand the second snapshot; constructing an incremental dataset profile ofthe dataset based upon the second set of data characteristics; andconstructing a second histogram of directory sizes that are modified,created, or deleted based upon the incremental dataset profile.
 4. Themethod of claim 1, comprising: constructing a graph of directory countsbased upon the baseline dataset profile.
 5. The method of claim 1,comprising: constructing a graph of file counts based upon the baselinedataset profile.
 6. The method of claim 1, comprising: evaluating thebaseline dataset profile to identify a service level objective violationof a data management service operating on the dataset.
 7. The method ofclaim 1, comprising: evaluating the baseline dataset profile to identifya quality of service false alarm.
 8. A non-transitory computer readablemedium having stored thereon executable code which when executed by acomputer causes the computer to: evaluate, by a storage server, asnapshot of a volume to identify a set of data characteristicscomprising file size information and directory size information;construct a baseline dataset profile of a dataset of data within thevolume based upon the set of data characteristics; and construct a firstgraph of directory counts based upon the baseline dataset.
 9. Thenon-transitory computer readable medium of claim 8, wherein the computerexecutable code causes the computer to: construct a second graph of filecounts based upon the baseline dataset profile.
 10. The non-transitorycomputer readable medium of claim 8, wherein the computer executablecode causes the computer to: construct a histogram of file sizes anddirectory sizes of the dataset based upon the baseline dataset profile.11. The non-transitory computer readable medium of claim 8, wherein thecomputer executable code causes the computer to: evaluate the snapshotand a second snapshot of the volume to identify a second set of datacharacteristics corresponding to differences between the snapshot andthe second snapshot; and construct an incremental dataset profile of thedataset based upon the second set of data characteristics.
 12. Thenon-transitory computer readable medium of claim 11, wherein the secondset of data characteristics correspond to directory creationinformation, directory deletion information, and directory modificationinformation.
 13. The non-transitory computer readable medium of claim11, wherein the second set of data characteristics correspond to filecreation information, file deletion information, and file modificationinformation.
 14. A computing device, comprising: a computer readablemedium comprising executable code; and a processor coupled to thecomputer readable medium, the processor configured to execute theexecutable code to cause the processor to: evaluate a snapshot of avolume to identify a set of data characteristics comprising file sizeinformation and directory size information; construct a baseline datasetprofile of a dataset of data within the volume based upon the set ofdata characteristics; and construct a first graph of file counts basedupon the baseline dataset profile.
 15. The computing device of claim 14,wherein the executable code causes the processor to: construct ahistogram of file sizes and directory sizes of the dataset based uponthe baseline dataset profile.
 16. The computing device of claim 14,wherein the set of data characteristics comprises a number of inodes anda size of inodes associated with the volume.
 17. The computing device ofclaim 14, wherein the executable code causes the processor to: evaluatethe snapshot and a second snapshot of the volume to identify a secondset of data characteristics corresponding to differences between thesnapshot and the second snapshot; and construct an incremental datasetprofile of the dataset based upon the second set of datacharacteristics.
 18. The computing device of claim 14, wherein theexecutable code causes the processor to: construct a second graph ofdirectory counts based upon the baseline dataset.
 19. The computingdevice of claim 14, wherein the executable code causes the processor to:evaluate the baseline dataset profile to identify a data managementrecommendation violation.
 20. The computing device of claim 14, whereinthe executable code causes the processor to: notify a user of the datamanagement recommendation violation.